Advanced Methods And Tools for ECG Data Analysis
Advanced Methods And Tools for ECG Data Analysis
Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
Artificial neural networks for automatic ECG analysis
IEEE Transactions on Signal Processing
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The ECG signal is a representation of bioelectrical activity of the heart's pumping action. The doctor regularly uses a temporal recording of ECG and waveforms characteristics to study and diagnose the overall heart functioning. In some heart diseases, the correct diagnosis in an early time is essential for the patient survival. This need leads to the necessity to automate normal beat signals discrimination from abnormal beat signals. In our study, we have chosen the Multilayer Perceptron (MLP) as a classifier for this type of signals into two categories: normal (N) and pathological (V). To train this network, we used the database "MIT BIH arrhythmia database." This training is improved using a novel swarm optimization algorithm called Artificial Bees Colony (ABC) inspired from the foraging intelligence of honey bees. The (ABC) has the advantage of using fewer control parameters compared to other swarm optimization Algorithms. We propose several algorithms to filter, detect R peaks and extract the features of cardiac cycles to get it ready to be classified.